International Journal of Advanced Engineering Application

ISSN: 3048-6807

Dependability Analysis of Bitcoin at the System Level under Eclipse and 51% Attacks

Author(s):Tannu Priya1, Sohamkar2, Abhinandan3

Affiliation: 1,2,3Department of Computer Science Engineering. 1,2,3Sai Vidya Institute of Technology, Karnataka, India

Page No: 10-13

Volume issue & Publishing Year: Volume 1 Issue 6, OCT-2024

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI:

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Abstract:
Bitcoin, a digital cryptocurrency rooted in Blockchain technology, has surged in popularity due to its decentralized nature. However, it is susceptible to certain cyberattacks, such as the 51% attack, where malicious actors can gain control over more than half of the networks computing power, thus having the ability to modify the blockchain. To execute this, attackers may initially perform an Eclipse attack, monopolizing communication channels to and from a Bitcoin node. In this paper, we analyze the reliability of the Bitcoin network when subjected to Eclipse and 51% attacks. We propose a hierarchical model using a continuous-time Markov chain (CTMC) for node-level dependability analysis and a multi-valued decision diagram (MDD) for system-level dependability assessment. The model is evaluated through case studies of Bitcoin systems with both homogeneous and heterogeneous nodes, analysing the influence of critical parameters on network dependability.

Keywords: Bitcoin, Dependability, Eclipse attack, Hierarchical modelling, 51% attack

Reference:

  • [1] S. Bag, S. Ruj, and K. Sakurai, “Bitcoin block withholding attack: Analysis and mitigation,” IEEE Transactions on Information Forensics and Security, vol. 12, no. 8, pp. 1967–1978, 2016.
  • [2] M. Bastiaan, Preventing the 51%-Attack: A Stochastic Analysis of Two Phase Proof of Work in Bitcoin. 2015. [Online]. Available: https://fmt.ewi.utwente.nl/media/175.pdf
  • (accessed June 2023).
  • [3] I. Eyal and E. G. Sirer, “Majority is not enough: Bitcoin mining is vulnerable,” in Proc. Int. Conf. Financial Cryptography and Data Security, Berlin, Heidelberg, 2014, pp. 436–454.
  • [4] M. A. Ferrag et al., “Blockchain technologies for the Internet of Things: Research issues and challenges,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2188–2204, 2018.
  • [5] J. Frizzo-Barker et al., “Blockchain as a disruptive technology for business: A systematic review,” International Journal of Information Management, vol. 51, p. 102029, 2020. doi: 10.1016/j.ijinfomgt.2019.10.014.
  • [6] A. Gervais, H. Ritzdorf, G. O. Karame, and S. Capkun, “Tampering with the delivery of blocks and transactions in Bitcoin,” in Proc. 22nd ACM SIGSAC Conf. Computer and Communications Security, Denver, USA, 2015, pp. 692–705.
  • [7] J. Göbel, H. P. Keeler, A. E. Krzesinski, and P. G. Taylor, “Bitcoin blockchain dynamics: The selfish-mine strategy in the presence of propagation delay,” Performance Evaluation, vol. 104, pp. 23–41, 2016.
  • [8] E. Heilman, A. Kendler, A. Zohar, and S. Goldberg, “Eclipse attacks on Bitcoin’s peer-to-peer network,” in Proc. 24th USENIX Security Symp., Washington, DC, USA, 2015, pp. 129–144.
  • [9] J. Kang et al., “Blockchain for secure and efficient data sharing in vehicular edge computing and networks,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4660–4670, 2018.
  • [10] J. V. Monaco, “Identifying Bitcoin users by transaction behavior,” in Biometric and Surveillance Technology for Human and Activity Identification XII, vol. 9457, Baltimore, USA, 2015, pp. 945704. doi: 10.1117/12.2177039.
  • [11] S. Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System. 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf
  • [12] F. Novoa, A. Orozco, R. Polanco, and A. Wightman, “The 51% attack on blockchains: A mining behavior study,” IEEE Access, vol. 9, pp. 140549–140564, 2021. doi: 10.1109/ACCESS.2021.3119291.
  • [13] F. Reid and M. Harrigan, “An analysis of anonymity in the Bitcoin system,” in Security and Privacy in Social Networks. Springer, New York, USA, 2013, pp. 197–223.
  • [14] A. Sapirshtein, Y. Sompolinsky, and A. Zohar, “Optimal selfish mining strategies in Bitcoin,” in Proc. Int. Conf. Financial Cryptography and Data Security, Berlin, Heidelberg, 2016, pp. 515–532.
  • [15] L. Xing, “Reliability in Internet of Things: Current status and future perspectives,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6704–6721, 2020. doi: 10.1109/JIOT.2020.2993216.
  • [16] L. Xing and S. V. Amari, Binary Decision Diagrams and Extensions for System Reliability Analysis. Scrivener Publishing, MA, 2015. doi: 10.1002/9781119178026.
  • [17] L. Xing and Y. Dai, “A new decision-diagram-based method for efficient analysis on multistate systems,” IEEE Transactions on Dependable and Secure Computing, vol. 6, no. 3, pp. 161–174, 2009.
  • [18] S. Zhang and J. H. Lee, “Double-spending with a sybil attack in the Bitcoin decentralized network,” IEEE Transactions on Industrial Informatics, vol. 15, no. 10, pp. 5715–5722, 2019.
  • [19] C. Zhou, L. Xing, and Q. Liu, “Dependability analysis of Bitcoin subject to eclipse attacks,” International Journal of Mathematical, Engineering and Management Sciences, vol. 6, no. 2, pp. 469–479, 2021.
  • [20] C. Zhou, L. Xing, J. Guo, and Q. Liu, “Bitcoin selfish mining modeling and dependability analysis,” International Journal of Mathematical, Engineering and Management Sciences, vol. 7, no. 1, pp. 16–27, 2022.
  • [21] C. Zhou, L. Xing, Q. Liu, and H. Wang, “Semi-Markov based dependability modeling of Bitcoin nodes under eclipse attacks and state-dependent mitigation,” International Journal of Mathematical, Engineering and Management Sciences, vol. 6, no. 2, pp. 480–492, 2021.
  • [22] C. Zhou, L. Xing, Q. Liu, and H. Wang, “Effective selfish mining defense strategies to improve Bitcoin dependability,” Applied Sciences, vol. 13, no. 1, p. 422, 2023. doi: 10.3390/app13010422